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# Founding Data Engineer, Pricing and Comps

- **Company:** ONE STOP COLLECTIBLE CORP
- **Location:** New York, NY, United States
- **Experience:** Expert
- **Salary:** $160,000.0 - $220,000.0
- **Contract:** Permanent contract
- **Skills:** Airflow, Computer Vision, Information Engineering, Extract Transform Load (ETL), Data Warehousing, Database Queries, Operational Databases, Apache Spark
- **Published:** October 30, 2026
- **Apply:** https://startup.jobs/founding-data-engineer-pricing-and-comps-clera-10617352

## About the Role

* At least 5 years of data engineering experience, including ownership of large-scale production ETL and data pipelines.
 * Strong SQL skills and experience with a modern data stack such as dbt, Airflow or Dagster, and Spark or similar tools.
 * Experience working with noisy or adversarial data in marketplaces, pricing, fraud, or a related domain.
 * Experience with statistical estimation or pricing models, including regression-based comparable sales logic.
 * Experience building entity resolution or record-matching systems at scale, plus production data quality monitoring.
 * Ability to make sound data quality tradeoffs and explain pricing confidence and uncertainty to non-technical stakeholders.
 * Experience processing data from multiple external sources and evaluating data products or integrations is valuable.
 * Pricing intelligence, data licensing, and early-stage startup experience are helpful.

## Description

Build the data foundation for pricing in an early-stage internet marketplace. You will turn noisy sold-listing data into reliable comparable items and price estimates, helping users and investors understand both the value and confidence of each estimate., * Build and own scheduled ingestion pipelines for sold-listing data from multiple marketplaces and external sources.

 * Design entity resolution systems that match inconsistent listing text and images to canonical items.
 * Develop valuation logic that weights comparable listings by recency, condition, and quantity.
 * Create production monitoring to detect data drift, fraud, stale comparisons, and integrity issues.
 * Partner with computer vision teammates to define how items are matched across sources with varying detail.
 * Make build-versus-buy decisions about data sources, licensing, and third-party pricing feeds.